1. Technical Field
The present invention relates to image processing, and more particularly, to a system and method for filtering noise from a medical image.
2. Discussion of the Related Art
Medical images are frequently noisy, due both to varying image data and artifacts resulting from an acquisition system such as an ultrasound, positron emission tomography (PET) or computed tomography (CT) scanner. Recently, nonlinear diffusion methods have proven useful for reducing image noise in many fields ranging from medical applications and image-sequence analysis to computer aided quality control and post-processing of noisy data.
One such nonlinear diffusion method presented by Perona and Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, July 1990, uses two design inputs for filtering an image. The first input is a function for mapping image intensities to diffusion constants and the second input is a stopping parameter which indicates how long the diffusion process is to run. A filtered image is then produced by taking its original grayscale values as an initial distribution and solving an anisotropic diffusion equation for a pre-specified amount of time.
A drawback of the technique proposed by Perona and Malik is that it does not it does not take into account inhomogeneous data sampling or enable the adjustment of tuning constants. In addition, it does not provide a method for choosing convergence criteria (e.g., the stopping parameter). Accordingly, there is a need for a technique of reducing noise in medical images such that noisy or high frequency objects are homogenized while preserving inter-object contrast.